Advanced data acquisition and intelligent data processing / Vladimír Haasz, and Kurosh Madani.

Format:

Book

Contributor:

Haasz, Vladimir, 1949- editor.

Madani, Kurosh, editor.

Series:

River Publishers series in information science and technology.

River Publishers Series in Information Science and Technology

Language:

English

Subjects (All):

Punched card systems.

Electronic data processing.

Database management.

Physical Description:

1 online resource (305 pages) : illustrations, graphs, tables.

Edition:

1st ed.

Place of Publication:

Gistrup, Denmark : River Publishers, [2014]

Summary:

The book arose based on the most interesting papers from this area published at IDAACS?2013 conference. However, the indivudual chapters include not only designed solution in wider context but also relevant theoretical parts, achieved results and possible future ways.

Contents:

Cover

Half Title Page - Advanced Data Acquisitionand Intelligent Data Processing

Series Page - RIVER PUBLISHERS SERIES IN INFORMATION SCIENCE ANDTECHNOLOGY

Title Page - Advanced Data Acquisitionand Intelligent Data Processing

Copyright Page

Table of Contents

Chapter 1 - Introduction

Chapter 2 - Waveform acquisition with resolutionsexceeding those of the ADCs employed

Abstract

2.1 Introduction

2.2 What resolutions are sufficient for the task at hand?

2.2.1. Horizontal resolution

2.2.2 Vertical resolution

2.2.3 Interrelation between the resolutions

2.3 One shot waveforms

2.3.1 Enhancing the vertical resolution

2.3.2 Enhancinng the horizoontal resoluution

2.3.3 Enhancing horizontal and vertical resolutions simultaneously

2.4 Repetitive waveforms

2.4.1 Enhancing the vertical resolution

2.4.2 Enhancing the horizontal resolution

2.4.3 Enhancing horizontal and vertical resolutions simultaneously

2.5 Repeatable waveforms

2.5.1 Vertical resolution

2.5.2 Horizontal resolution

2.5.3 Enhancing horizontal and vertical resolutions simultaneously

2.5.4 Application examples for ultrasonic NDE

2.6 Summary and conclusions

References

Biographies

Chapter 3 - Different appliance identification methods innon-intrusive appliance load monitoring

3.1 The necessity for energy monitoring systems

3.2 Energy monitoring systems

3.3 Ellectrical ennergy conssumption appliances

3.4 Appliance identification methods

3.4.1 Identification methods based on power changes

3.4.2 Identification methods based on current harmonics

3.4.3 Identification based on V-I trajectory

3.4.4 Identification based on electromagnetic interference

3.4.5 Identification based on switching voltage and currenttransients

3.4.6 Shape power change transient methods.

3.4.7 Combined identification methods

3.5 Comparison of different methods

Acknowledgement

Chapter 4 - Design and testing of an electronic nosesensitive to the aroma of truffles

4.1 Introduction

4.1.1 Overview of e-nose systems

4.1.2 Application of electronic noses to food products

4.1.3 Contribution and organization of the present work.

4.2 Ascomycete tuber and its volatile compounds

4.2.1 Truffle species

4.2.2 Key truffle volatiles

4.3 Design of the e-nose system

4.3.1 Sensor array

4.3.2 Sample compartment and sensor chamber

4.3.3 Data acquisition and processing system

4.4 Sensor-array response to truffle aroma

4.5 Discrimination of sensor patterns

4.5.1 Feature extraction from the measurement data

4.5.2 Principal components analysis and k-NN

4.6 Conclusion

Chapter 5 - Data acquisition for ultrasonic transducerevaluation under spread spectrum excitation*

5.1 Introduction

5.2 Spread spectrum signals

5.3 Ultrasound transduction performance

5.3.1 Transducer frequency response

5.3.2 Data acquisition for transducer impedance measurement

5.3.3 Matching the excitation generator and transducer

5.3.4 Transduction measurement

5.4 Trransducerdirectivityperformannce

5.4.1 Directivitty estimation principles

5.4.2 Directivity measurement techniques

5.5 Ultrasonic signals acquisition system

5.5.1 System structure and data processing

5.5.2 Directivity investigation results

5.6 Summary

Chapter 6 - Optimal information fusion in stochasticunknown input observers network

6.1 Introduction

6.2 Decentralized State and Input Estimation Problem

6.3 State and input signal invariant estimation.

6.3.1 Reduced order regularized observer design

6.3.2 Input signal identification

6.4 Stochastic optimal unknown input observer design

6.4.1 Observer structural design

6.4.2 Stochastic observer optimization

6.5 Optimal information fusion in stochastic observer network

6.5.1 Information fusion for distributed stochastic observers

6.5.2 Information fusion in consensus observer network

Biography

Chapter 7 - Odor classification by neural networks∗

7.1 Introduction

7.2 Human olfactory processes

7.3 Electronic nose system

7.3.1 Odor delivery system

7.3.2 Odor sensor array

7.3.3 Data recording

7.3.4 Data processing

7.4 Principle of odor sensing

7.4.1 Principle of MOG sensors

7.4.2 Principle of QCM sensors

7.5 Odor sensing system

7.6 Classification method of odor data

7.7 Classification results using MOG sensors

7.8 Classification results using QCM sensors for mixeddor ata

7.8.1 Training for classification of odors

7.8.2 Classification results and discussion for mixed odor data

7.9 Conclusions

Chapter 8 - ANFIS based approach for improvedmultisensors signal processing

8.1. Introduction

8.2. Identification methods

8.2.1 Artificial neural networks for identification

8.2.2 Neuron model

8.2.3 Activation functions

8.2.4 Adaptive neuro-fuzzy inference system for identification

8.3. Neural network method for identification of themultisensor's individual characteristic curve

8.4. Improved approach for identification of the multisensor'sindividual characteristic curve

8.5. Experimental studies

8.6. Experimental results

8.7. Conclusion

Chapter 9 - FPGA-based ANFIS linearizerfor measurement systems

9.1 Introduction.

9.2 The linearizer in a measurement system

9.3 Fuzzy modelling

9.3.1 Fuzzy inference system

9.3.2 Takagi-Sugeno fuzzyy model

9.3.3 ANFIS arrchitecture

9.4 Liinearization ANFIS modelling setup

9.5 FPGA design and implementation

9.5.1 ANFIS circuit digital elements

9.5.2 FPGA implementation

9.6 Conclusion

Chapter 10 - Identification of systems using Volterra modelin time and frequency domain

10.1 Introduction

10.2 Volterra models and identification of nonlineardynamical systems

10.3 Theoretical foundation of the interpolation methodof the system's identification

10.3.1 Numerical differentiation for interpolation method of nonlinearsystems identification

10.3.2 Identification of dynamical systems as a Volterra model intime domain using poly-impulse signals

10.3.3 Identification of dynamical systems as a Volterra model infrequency domain using polyharmonic signals

10.3.4 The theorem about choosing the test signals frequencies

10.4 Simulation of the identification method

10.4.1 The techniques of test system identification

10.4.2 Error estimation of the identification methods

10.4.3 Analysis of the test system identification accuracy and noiseimmunity for interpolation method in time domain

10.4.4 Comparison of the identification methods in time domain

10.4.5 Analysis of the test system identification accuracy and noiseimmunity for interpolation method in frequency domain

10.4.6 Analysis of the test system noise immunity for interpolationmethod in frequency domain

10.5 The algorithm and toolkit of radiofrequency channelidentification

10.6 Conclusion

Chapter 11 - Training cellular automata for hyperspectral image segmentation*

11.1 Introduction.

11.2 Evolutionary cellular Automata based segmentation

11.2.1 CA definition

11.2.2 Segmentation

11.2.3 CA training

11.3 Validation over synthetic images

11.4 Application example: Salinas

11.5 Application example: Pavia

11.6 Conclusions

Index

About the editors.

Notes:

Includes index.

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

1-000-79530-6

1-00-333702-3

1-000-79208-0

1-003-33702-3

87-93102-74-7

9781003337027

OCLC:

1347185506